A Novel Approach to Classification of Soil Type for Crop Agronomy Using Decision Tree with Multiplayer Neural Network
Keywords:
Multilayer neural networks (MLNN), Soil Classification, Crop suggestion, decision tree, irrigation systems.Abstract
The present research work focuses on developing a novel framework for the building of a decision tree model that employ multilayer neural network(MLNN) to classify the soil of a given geographical area. The biggest challenge that farmers in India face is selecting suitable crops to planting as they’re unaware of the soil types of their region and its properties. This research work proposes a navel approach using decision tree to classify the soil type, utilizing a multilayer neural network. Once the soil types are identified, the proposed model helps to select the most suitable crops to cultivate. The proposed model also proposes to apply the most suitable fertilizers to the identified crops to provide essential nutrients, promoting plant growth and increasing crop yield, and also suggest appropriate irrigation system (drip, sprinkler, wells, tube wells etc) for the selected crops of that specific region. To get started with the soil type classification procedure, the necessary dataset is downloaded. The present research uses a proposed algorithm MLNN for classifying the different types of soil for crop agronomy. The superiority of Multilayer Neural Network in accuracy over the existing algorithms, namely SVM, KNN, Decision Tree (DT), Bayesian Models, Ensemble learning algorithms etc highlights the effectiveness of proposed model in capturing pattern within the data. Finally our proposed model helps in providing more reliable guidance to the agronomists to maximize the production.
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Hamzah, Mohammed Diqi, Antony David, “Effective Soil Type Classification using Convolution Neural Network”, International Journal of Informatics and Computation (IJICOM), Vol.3,No.1, August 2021, ISSN:2685-8711, E-ISSN:2714-5263
Ayela Tesema Chala, Richard Ray, “ Assessing the performance of Machine Learning Algorithm for Soil Classification using Cone Penetration Test Data”, Journal of Applied Science, Volume.13,issue:09, May 2023,5758,https://doi.org/10.3340/amm/3095758
Vrushal Milan Dolas, Prof.Uday Joshi, “A Novel Approach for Classification of Soil and Crop Prediction”, International Journal of Computer Science and Mobile Computing (IJCSMC), Vol.7, Issue 3,pp.20-24, March 2018,ISSN 2320-0887.
RushikaGadge, JuileeKulkarni, Pooja More, Sachee Nene, Priya. R, “ Prediction of Crop yield using Machine Learning”, International Journal Research Journal of Engineering and Technology (IRJET), volume:05, issue: 02, pp. 2237-2239, Feb 2018, e-ISSN: 2395-0056.6.
Tanuja K.Fegade and B.V.Pawar,” Crop prediction using Artificial neural network and support vector machine”, Data management, Analytics and Innovation, proceeding of ICDMAI 2019, volume 2, Springer.
Rub G. “ Data mining of agricultural yield data: A Comparison of regression models”, 9th International conference, vol : 56, pp. 24-37.
Kushwaha A.K. and Bhattacharya.S , “ Crop yield prediction using Agro Algorithms in Hadoop”, International journal of computer science , Information Technology & Security (IJCSITS),2015, 5(2), pp.271-274.
N.Saranya and A.Mythili, “Classification of Soil and Crop Suggestion using Machine Learning Technique”,International Journal of Engineering Research & Technology (IJERT),Vol. 9 Issue 02, pp. 671-673 February-2020, ISSN: 2278-0181
Chandan1, Ritula Thakur,” Recent Trends Of Machine Learning In Soil Classification: A Review”, International Journal of Computational Engineering Research (IJCER), Volume, 08, Issue, 9, pp. 25-31,Sepetember – 2018, ISSN (e): 2250 – 3005.
AshwiniRao, Janhavi V, AbhishekGowda N.S and Mrs.RafegaBeham,”Machine Learning in Soil Classification and crop detection”, International Journal of Scientific Research & Development, Volume:4, Issue: 1,pp.792-794, April 2016, ISSN (online): 2321-0613
Sk Al Zaminur Rahman, Kaushik Chandra Mitra, and S.M. Mohidul Islam, “Soil Classification Using Machine Learning Methods and Crop Suggestion Based on Soil Series” , 21st International Conference of Computer and Information Technology (ICCIT),Date of Conference: 21-23 Dec. 2018 ,Date Added to IEEE Xplore: 04 February 2019, ISBN Information:INSPEC Accession Number: 18432260 ,Publisher: IEEE
J.Padrian, B.Minasny and A.B.McBratney, “Using Deep Learning to predict soil properties from regional spectral data”, Geode ram Regional Publication, Volume 16, March 2019,e00198
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